# Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""A demo script showing how to use the uisrnn package on toy data."""

import numpy as np
from functools import partial
from torch.utils.tensorboard import SummaryWriter
import torch.multiprocessing as mp
mp = mp.get_context('forkserver')

import uisrnn


SAVED_MODEL_NAME = 'saved_model.uisrnn'
NUM_WORKERS = 2


def diarization_experiment(model_args, training_args, inference_args):
  """Experiment pipeline.

  Load data --> train model --> test model --> output result

  Args:
    model_args: model configurations
    training_args: training configurations
    inference_args: inference configurations
  """
  # data loading
  train_data = np.load('./data/toy_training_data.npz', allow_pickle=True)
  test_data = np.load('./data/toy_testing_data.npz', allow_pickle=True)
  train_sequence = train_data['train_sequence']
  train_cluster_id = train_data['train_cluster_id']
  test_sequences = test_data['test_sequences'].tolist()
  test_cluster_ids = test_data['test_cluster_ids'].tolist()

  # model init
  model = uisrnn.UISRNN(model_args)
  # model.load(SAVED_MODEL_NAME) # to load a checkpoint
  # tensorboard writer init
  writer = SummaryWriter()

  # training
  for epoch in range(training_args.epochs):
    stats = model.fit(train_sequence, train_cluster_id, training_args)
    # add to tensorboard
    for loss, cur_iter in stats:
      for loss_name, loss_value in loss.items():
        writer.add_scalar('loss/' + loss_name, loss_value, cur_iter)
    # save the mdoel
    model.save(SAVED_MODEL_NAME)

  # testing
  predicted_cluster_ids = []
  test_record = []
  # predict sequences in parallel
  model.rnn_model.share_memory()
  pool = mp.Pool(NUM_WORKERS, maxtasksperchild=None)
  pred_gen = pool.imap(
      func=partial(model.predict, args=inference_args),
      iterable=test_sequences)
  # collect and score predicitons
  for idx, predicted_cluster_id in enumerate(pred_gen):
    accuracy = uisrnn.compute_sequence_match_accuracy(
        test_cluster_ids[idx], predicted_cluster_id)
    predicted_cluster_ids.append(predicted_cluster_id)
    test_record.append((accuracy, len(test_cluster_ids[idx])))
    print('Ground truth labels:')
    print(test_cluster_ids[idx])
    print('Predicted labels:')
    print(predicted_cluster_id)
    print('-' * 80)

  # close multiprocessing pool
  pool.close()
  # close tensorboard writer
  writer.close()

  print('Finished diarization experiment')
  print(uisrnn.output_result(model_args, training_args, test_record))


def main():
  """The main function."""
  model_args, training_args, inference_args = uisrnn.parse_arguments()
  diarization_experiment(model_args, training_args, inference_args)


if __name__ == '__main__':
  main()
